Generally, a lung nodule in a first computed tomography image data set and in a follow-up computed tomography image data set is manually or automatically selected and the selected lung nodule is segmented separately in both computed tomography image data sets. The volume of the segmented lung nodule in the first computed tomography image data set and the volume of the segmented lung nodule in the follow-up computed tomography image data set are determined and compared for determining growth or shrinkage of the lung nodule.
Since the segmentation is performed on each computed tomography image data set separately, yielding a volume number assigned to a lung nodule at each point in time, the segmented volume can be different, even if the same lung nodule having the same size and shape is present in both computed tomography image data sets, for example, because of small variations in the image data set values due to noise, metal or other artifacts, which can be generated during the reconstruction of the computed tomography image data sets. From a mathematical point of view, this can be expressed as an ill-posed problem, since small variations in the input data, i.e. the computed tomography image data set values, may lead to large variations in the output data, i.e. the segmented volumes, causing the accuracy of determining a modification of a size of the lung nodule to be reduced.